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1. Grid-aware aggregation and realtime disaggregation of distributed energy resources in radial networks

   https://doi.org/10.1109/TPWRS.2021.3121215  

   Nazir, Nawaf ; Almassalkhi, Mads ( October 2021 , IEEE Transactions on Power Systems)

   Dispatching a large fleet of distributed energy resources (DERs) in response to wholesale energy market or regional grid signals requires solving a challenging disaggregation problem when the DERs are located within a distribution network. This manuscript presents a computationally tractable convex inner approximation for the optimal power flow (OPF) problem that characterizes a feeders aggregate DERs hosting capacity and enables a realtime, grid-aware dispatch of DERs for radial distribution networks. The inner approximation is derived by considering convex envelopes on the nonlinear terms in the AC power flow equations. The resulting convex formulation is then used to derive provable nodal injection limits, such that any combination of DER dispatches within their respective nodal limits is guaranteed to be AC admissible. These nodal injection limits are then used to construct a realtime, open-loop control policy for dispatching DERs at each location in the network to collectively deliver grid services. The IEEE-37 distribution network is used to validate the technical results and highlight various use-cases. 

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2. Integration of Centralized and Distributed Methods to Mitigate Voltage Unbalance Using Solar Inverters

   https://doi.org/10.1109/TSG.2022.3214192  

   Girigoudar, Kshitij ; Yao, Mengqi ; Mathieu, Johanna L. ; Roald, Line A. ( May 2023 , IEEE Transactions on Smart Grid)

   Growing penetrations of single-phase distributed generation such as rooftop solar photovoltaic (PV) systems can increase voltage unbalance in distribution grids. However, PV systems are also capable of providing reactive power compensation to reduce unbalance. In this paper, we compare two methods to mitigate voltage unbalance with solar PV inverters: a centralized optimization-based method utilizing a three-phase optimal power flow formulation and a distributed approach based on Steinmetz design. While the Steinmetz-based method is computationally simple and does not require extensive communication or full network data, it generally leads to less unbalance improvement and more voltage constraint violations than the optimization-based method. In order to improve the performance of the Steinmetz-based method without adding the full complexity of the optimization-based method, we propose an integrated method that incorporates design parameters computed from the set-points generated by the optimization-based method into the Steinmetz-based method. We test and compare all methods on a large three-phase distribution feeder with time-varying load and PV data. The simulation results indicate trade-offs between the methods in terms of computation time, voltage unbalance reduction, and constraint violations. We find that the integrated method can provide a good balance between performance and information/communication requirements. 

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3. Towards Optimal Kron-based Reduction Of Networks (Opti-KRON) for the Electric Power Grid

   https://doi.org/10.1109/CDC51059.2022.9992730  

   Chevalier, Samuel ; Almassalkhi, Mads R. ( December 2022 , 2022 IEEE 61st Conference on Decision and Control)

   For fast timescales or long prediction horizons, the AC optimal power flow (OPF) problem becomes a computational challenge for large-scale, realistic AC networks. To overcome this challenge, this paper presents a novel network reduction methodology that leverages an efficient mixed-integer linear programming (MILP) formulation of a Kron-based reduction that is optimal in the sense that it balances the degree of the reduction with resulting modeling errors in the reduced network. The method takes as inputs the full AC network and a pre-computed library of AC load flow data and uses the graph Laplacian to constraint nodal reductions to only be feasible for neighbors of non-reduced nodes. This results in a highly effective MILP formulation which is embedded within an iterative scheme to successively improve the Kron-based network reduction until convergence. The resulting optimal network reduction is, thus, grounded in the physics of the full network. The accuracy of the network reduction methodology is then explored for a 100+ node medium-voltage radial distribution feeder example across a wide range of operating conditions. It is finally shown that a network reduction of 25-85% can be achieved within seconds and with worst-case voltage magnitude deviation errors within any super node cluster of less than 0.01pu. These results illustrate that the proposed optimization-based approach to Kron reduction of networks is viable for larger networks and suitable for use within various power system applications. 

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4. Strategic Behavior of Distributed Energy Resources in Energy and Reserves Co-Optimizing Markets

   https://doi.org/10.1109/CDC.2018.8619550  

   Yanikara, Fatma Selin ; Andrianesis, Panagiotis ; Caramanis, Michael ( January 2019 , 2018 IEEE Conference on Decision and Control (CDC), Miami Beach, FL, 2018, pp. 4875-4881. doi: 10.1109/CDC.2018.8619550)

   null (Ed.)

   We consider decentralized scheduling of Distributed Energy Resources (DERs) in a day-ahead market that clears energy and reserves offered by both centralized generators and DERs. Recognizing the difficulty of scheduling transmission network connected generators together with distribution feeder connected DERs that have complex intertemporal preferences and dynamics, we propose a tractable distributed algorithm where DERs self-schedule based on granular Distribution Locational Marginal Prices (DLMPs) derived from LMPs augmented by distribution network costs. For the resulting iterative DER self-scheduling process, we examine the opportunity of DERs to engage in strategic behavior depending on whether DERs do or do not have access to detailed distribution feeder information. Although the proposed distributed algorithm is tractable on detailed real-life network models, we utilize a simplified T&D network model to derive instructive analytical and numerical results on the impact of strategic DER behavior on social welfare loss, and the distribution of costs and benefits to various market participants. 

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5. Deep Policy Gradient for Reactive Power Control in Distribution Systems

   Q. Yang, A. Sadeghi ( January 2020 , Proceedings of IEEE Smartgridcom Conference)

   null (Ed.)

   Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, motivates well fast, efficient, and robust reactive power control. Optimal reactive power control is possible in theory by solving a non-convex optimization problem based on the exact model of distribution flow. However, lack of high-precision instrumentation and reliable communications, as well as the heavy computational burden of non-convex optimization solvers render computing and implementing the optimal control challenging in practice. Taking a statistical learning viewpoint, the input-output relationship between each grid state and the corresponding optimal reactive power control (a.k.a., policy) is parameterized in the present work by a deep neural network, whose unknown weights are updated by minimizing the accumulated power loss over a number of historical and simulated training pairs, using the policy gradient method. In the inference phase, one just feeds the real-time state vector into the learned neural network to obtain the ‘optimal’ reactive power control decision with only several matrix-vector multiplications. The merits of this novel deep policy gradient approach include its computational efficiency as well as robustness to random input perturbations. Numerical tests on a 47-bus distribution network using real solar and consumption data corroborate these practical merits. 

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